Defining D-irAEs: consensus-based disease definitions for the diagnosis of dermatologic adverse events from immune checkpoint inhibitor therapy
Bibliographic record
Abstract
With an increasing number of patients eligible for immune checkpoint inhibitors, the incidence of immune-related adverse events (irAEs) is on the rise. Dermatologic immune-related adverse events (D-irAEs) are the most common and earliest to manifest, often with important downstream consequences for the patient. Current guidelines lack clarity in terms of diagnostic criteria for D-irAEs. The goal of this project is to better define D-irAE for the purposes of identification, diagnosis, and future study of this important group of diseases.The objectives of this project were to develop consensus guidance for an approach to D-irAEs including disease definitions and severity grading. Knowing that consensus among oncologists, dermatologists, and irAE subspecialists would be critical for usability, we formed a Dermatologic irAE Disease Definition Panel. The panel was composed of 34 experts, including oncologists, dermatologists, a rheumatologist, and an allergist/immunologist from 22 institutions across the USA and internationally. A modified Delphi consensus process was used, with two rounds of anonymous ratings by panelists and two virtual meetings to discuss areas of controversy. Panelists rated content for usability, appropriateness, and accuracy on 9-point scales in electronic surveys and provided free text comments. A working group aggregated survey responses and incorporated them into revised definitions. Consensus was based on numeric ratings using the RAND/UCLA Appropriateness Method with prespecified definitions.Following revisions based on panelist feedback, all items received consensus in the second round of ratings. Consensus definitions were achieved for 10 core D-irAE diagnoses: ICI-vitiligo, ICI-lichen planus, ICI-psoriasis, ICI-exanthem, ICI-bullous pemphigoid, ICI-Grover's, ICI-eczematous, ICI-eruptive atypical squamous proliferation, ICI-pruritus without rash, and ICI-erosive mucocutaneous. A standard evaluation for D-irAE was also found to reach consensus, with disease-specific exceptions detailed when necessary. Each disorder's description includes further details on disease subtypes, symptoms, supportive exam findings, and three levels of diagnostic certainty (definite, probable, and possible).These consensus-driven disease definitions standardize D-irAE classification in a useable framework for multiple disciplines and will be the foundation for future work. Given consensus on their accuracy and usability from a representative panel group, we anticipate that they can be used broadly across clinical and research settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.140 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".